High-level Data Access Based on Query Rewritings

1. High-level Data Access Based on Query Rewritings Ekaterina Stepalina Higher School of Economics

2. High-Level Data Access • Concentration on application domain tasks • Abstraction from data sources • Efficient work • Research • This problem is actively considered on modern scientific conferences on knowledge representation and ontologies – OWLED (2009), (ICDE IIMAS, 2008) , the Semantic Web magazine (2011 – the Mastro System) • W3C developedOWL 2, OWL 2 QL (2008) and etc.

3. Ontology-Based Data Access (OBDA) • Large amounts of data (distributed, inconsistent) • Main task – query answering (domain-oriented and efficient)

4. What is Ontology? • Ontology is a knowledge domain described on some knowledge representation language. • Entity-Relationship and UML Class diagrams can be seen as ontology languages.

5. Logic-Based Knowledge Representation • Enables semantic processing of data • Enables inference of implicit knowledge • Well studied and actively developed • Description logics (Baader,1999), esp. DL-Lite • Standardized • OWL 2 Profiles

6. DL-Lite Best Suites for OBDA • High expressive and computationally efficient • Allows delegating query answering to DBMSs and using all advantages of modern relational technologies • Supported by the W3C standard - OWL 2 QL

7. Query Answering Problem • Given a query and an n-tuple of objects fromA. Decide, whether , or the n-tuple is the answer for with respect to K. For knowledge represented in DL-Lite, we can formulate queries in domain concepts, translate them into ordinary SQL queries and perform over separate databases.

8. OBDA System Architecture • Ontology Editor • OBDA-Enabled Reasoner • Mapping Processor • Data Source Manager • Consistency Checker

9. Query Rewritings • OBDA-Enabled Reasoner rewrites the initial ontology query into a set of UCQ (union conjunctive query). • Mapping Processor builds an SQL from UCQ and given mappings. • The initial query syntax may differ (SparQL, datalog query, etc.)

10. TBox and ABox in DL • TBox is a finite set of concept and role inclusion axioms: • ABox is a finite set of assertions: • Where - object’s name, A – concept name, P – role name, q – integer.

11. Interpretation • Interpretation (the particular instance of KB) is a pair if non-empty domain and an interpretation function : , , and . • UNA (unique name assumption):

12. OWL 2 QL • UNA is ignored; (in)equality must be defined explicitly • Language expressive power reduced up to (other designation - ). • Basic conceptual modeling relations are available: (A)sym, (Ir)Ref, Tran • Main constraints of : • Functional relations cannot be defined • Particular roles cannot be assigned only to specific concepts, all roles are applied to all concepts • Disjunction coverage of knowledge domain cannot be defined

13. Query Rewriting Sample • RDB tables: Person(name, age), Lives (person, city), Manages (boss, employee). • Query:Get the names and ages of all people living in the same city with their boss. • UCQ: • SimplifiedUCQ: • SQL query: • SELECT P.name, P.age • FROM Person P, Manages M, Lives L1, Lives L2 • WHERE P.name=L1.person AND P.name=M.employee AND M.boss=L2.person AND L1.city=L2.city

14. Query Rewriting Algorithms • CGLLR (Calvanese et al., 2007) - Applies all suitable TBox axioms to - Replaces axioms containing existential qualifications with another 3 axioms, which increases the number of UCQ • RQR (Pérez-Urbina, Horrocks, Motik, 2009) • Generates clauses from TBox assertions and then resolve clauses with query • Potentially supports more expressive DLs

15. Query Rewriting Benchmark • 9 ontologies with axioms, containing-existential qualification: • Vicodi (V) • Stock exchange (S) • University (U,UX) • Adolena (A,AX) • Synthetic (P1, P5,P5X)

16. Comparison Results • RQRis more preferable to implement in OBDA-enabled reasoners, thanCGLLR: • Generates less UCQ, especially for ontologies with large number of existential qualifications • May be further optimized and advanced to more expressive DLs, than

17. Produced Rewritings

18. Running Time, ms

19. Current Work • Preparing an ontology for a real application – interactive television platform (IPTV) for testing algorithms on real data • Optimizing RQR – reducing the number of generated clauses • Main idea – not advance RQR, but support more expressiveness and all OWL 2 QL constructors in powerful mappings

20. References • The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, 2002. ISBN 0521781760. Edited by F. Baader, D. Calvanese, D. McGuinness, D. Nardi, P. F. Patel-Schneider. • F. Baader. Logic-Based Knowledge Representation. In M.J. Wooldridge and M. Veloso, editors, Artificial Intelligence Today, Recent Trends and Developments, number 1600 in Lecture Notes in Computer Science, pages 13–41. Springer Verlag, 1999. • Artale, A.; Calvanese, D.; Kontchakov, R. and Zakharyaschev, M. (2009) The DL-Lite family and relations. Journal of Artificial Intelligence Research 36 (1), pp. 1-69. ISSN 1076-9757. • H.P´erez-Urbina, I.Horrocks, and B.Motik. Efficient Query Answering for OWL 2. In Proceedings of the 8th International Semantic Web Conference (ISWC2009), Chantilly, Virginia, USA, 2009. • H.P´erez-Urbina, B.Motik, and I.Horrocks. Tractable Query Answering and Rewriting under Description Logic Constraints. JournalofAppliedLogic, 2009.

21. High-level Data Access Based on Query Rewritings Questions?